{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 提取文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"status\": \"running\",\n",
      "    \"createdDateTime\": \"2020-10-24T15:06:34Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-24T15:06:34Z\"\n",
      "}\n",
      "{\n",
      "    \"status\": \"succeeded\",\n",
      "    \"createdDateTime\": \"2020-10-24T15:06:34Z\",\n",
      "    \"lastUpdatedDateTime\": \"2020-10-24T15:06:35Z\",\n",
      "    \"analyzeResult\": {\n",
      "        \"version\": \"3.0.0\",\n",
      "        \"readResults\": [\n",
      "            {\n",
      "                \"page\": 1,\n",
      "                \"angle\": 1.3638,\n",
      "                \"width\": 500,\n",
      "                \"height\": 375,\n",
      "                \"unit\": \"pixel\",\n",
      "                \"lines\": [\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            77,\n",
      "                            261,\n",
      "                            140,\n",
      "                            262,\n",
      "                            140,\n",
      "                            286,\n",
      "                            77,\n",
      "                            284\n",
      "                        ],\n",
      "                        \"text\": \"sleep\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    77,\n",
      "                                    261,\n",
      "                                    139,\n",
      "                                    262,\n",
      "                                    139,\n",
      "                                    286,\n",
      "                                    77,\n",
      "                                    284\n",
      "                                ],\n",
      "                                \"text\": \"sleep\",\n",
      "                                \"confidence\": 0.986\n",
      "                            }\n",
      "                        ]\n",
      "                    },\n",
      "                    {\n",
      "                        \"boundingBox\": [\n",
      "                            215,\n",
      "                            257,\n",
      "                            348,\n",
      "                            257,\n",
      "                            348,\n",
      "                            282,\n",
      "                            215,\n",
      "                            282\n",
      "                        ],\n",
      "                        \"text\": \"Don't sleep!\",\n",
      "                        \"words\": [\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    216,\n",
      "                                    259,\n",
      "                                    273,\n",
      "                                    258,\n",
      "                                    272,\n",
      "                                    282,\n",
      "                                    216,\n",
      "                                    281\n",
      "                                ],\n",
      "                                \"text\": \"Don't\",\n",
      "                                \"confidence\": 0.939\n",
      "                            },\n",
      "                            {\n",
      "                                \"boundingBox\": [\n",
      "                                    277,\n",
      "                                    258,\n",
      "                                    348,\n",
      "                                    258,\n",
      "                                    348,\n",
      "                                    283,\n",
      "                                    277,\n",
      "                                    283\n",
      "                                ],\n",
      "                                \"text\": \"sleep!\",\n",
      "                                \"confidence\": 0.983\n",
      "                            }\n",
      "                        ]\n",
      "                    }\n",
      "                ]\n",
      "            }\n",
      "        ]\n",
      "    }\n",
      "}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "endpoint = \"https://shijue01.cognitiveservices.azure.com/\"\n",
    "text_recognition_url = endpoint + \"/vision/v3.0/read/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to recognize.\n",
    "image_url = \"https://ss1.bdstatic.com/70cFvXSh_Q1YnxGkpoWK1HF6hhy/it/u=1770444190,610616913&fm=26&gp=0.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"8a2abe4e786c40c1936efe083f794112\"}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(text_recognition_url, headers=headers, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# Extracting text requires two API calls: One call to submit the\n",
    "# image for processing, the other to retrieve the text found in the image.\n",
    "\n",
    "# Holds the URI used to retrieve the recognized text.\n",
    "operation_url = response.headers[\"Operation-Location\"]\n",
    "\n",
    "# The recognized text isn't immediately available, so poll to wait for completion.\n",
    "analysis = {}\n",
    "poll = True\n",
    "while (poll):\n",
    "    response_final = requests.get(\n",
    "        response.headers[\"Operation-Location\"], headers=headers)\n",
    "    analysis = response_final.json()\n",
    "    \n",
    "    print(json.dumps(analysis, indent=4))\n",
    "\n",
    "    time.sleep(1)\n",
    "    if (\"analyzeResult\" in analysis):\n",
    "        poll = False\n",
    "    if (\"status\" in analysis and analysis['status'] == 'failed'):\n",
    "        poll = False\n",
    "\n",
    "polygons = []\n",
    "if (\"analyzeResult\" in analysis):\n",
    "    # Extract the recognized text, with bounding boxes.\n",
    "    polygons = [(line[\"boundingBox\"], line[\"text\"])\n",
    "                for line in analysis[\"analyzeResult\"][\"readResults\"][0][\"lines\"]]\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image)\n",
    "for polygon in polygons:\n",
    "    vertices = [(polygon[0][i], polygon[0][i+1])\n",
    "                for i in range(0, len(polygon[0]), 2)]\n",
    "    text = polygon[1]\n",
    "    patch = Polygon(vertices, closed=True, fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(vertices[0][0], vertices[0][1], text, fontsize=20, va=\"top\")\n",
    "plt.show()"
   ]
  }
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